Execution-harness refinement
Lets LLM agents automatically evolve their prompts, tools, and memory for open-world tasks without ground-truth verifiers.
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Coding / product dossier
A recursive framework that lets LLM agents refine their prompts, tools, and memory without verifiers.
Product brief
Harness² from Google Research is a plug-and-play recursive agent harnessing framework that lets LLM agents automatically evolve their execution harnesses (prompts, tools, memory) for open-world tasks—without ground-truth verifiers. Dual recursion: task-level Propose-Probe-Compose refinement + harnessing-level accumulation of reusable experience. Up to +11.5 points on professional benchmarks like WorkBuddy-Bench.
Why we selected it
Technically distinctive research tooling that improves agents' prompts, tools, and memory without ground-truth verifiers. Its reusable-experience mechanism and reported benchmark gains offer substance for researchers and
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Capability scan
Only capabilities supported by the product information we collected are listed here.
Lets LLM agents automatically evolve their prompts, tools, and memory for open-world tasks without ground-truth verifiers.
Uses a Propose-Probe-Compose process to refine how agents approach tasks.
Accumulates reusable experience at the harnessing level as part of its dual-recursion framework.
Best-fit use cases
FAQ
Harness² is a plug-and-play recursive agent harnessing framework from Google Research that lets LLM agents evolve their execution harnesses.
No. The framework is described as supporting open-world tasks without ground-truth verifiers.
It combines task-level Propose-Probe-Compose refinement with harnessing-level accumulation of reusable experience.
The supplied description reports gains of up to 11.5 points on professional benchmarks such as WorkBuddy-Bench.